The result of investigations by means of modalities which produce section images, such as CT scanners, magnetic resonance appliances and ultrasound appliances generally include a number of series with a large number of section images of the relevant examination object. For further planning of the examination and/or in order to produce a diagnosis, this section image data must in many cases be processed further during the examination itself, or immediately after the examination. The so-called “segmentation” of anatomical structures plays a major role in the further processing of this section image data. A segmentation process such as this is used to break down the image data of the examination object such that specific object elements of an examination object, that is to say specific anatomical structures which are the focal point of the respective examination, are separated from the rest of the image data. One obvious example of this is separation of the bone structure of the pelvis from a CT or MR section image data record of a patient's lower body.
A further example is contrast agent angiography by way of computed tomography. In an examination such as this it is worthwhile, and maybe absolutely essential, to remove the interfering bone components from the volume data record in order subsequently to make it possible to produce diagnostically valid MIP representations (MIP=Maximum Intensity Projection) or other result images. This is particularly important for those examinations in the area of the skull or spinal column. Good segmentation of the already existing section image data also plays an important role in other areas of angiography, for operation planning or else for selecting the modality for further detailed images of an anatomical structure that is of interest.
One relatively simple segmentation algorithm is the so-called “threshold value method”. This method operates in such a way that the intensity values (which are referred to as “Hounsfield values” in computer tomography) of the individual voxels, that is to say of the individual 3-D pixels, are compared with a fixed threshold value setting. If the value of the voxel is above the threshold value, then this voxel is added to a specific structure. However, this method can be used for magnetic resonance scans, particularly for contrast agent examinations or in order to separate the external skin surface from the environment.
In the case of computed tomography scans, this method may additionally also be used for separation of bone structures. This method is not suitable for separation of other tissue types. Furthermore, unfortunately, in many cases it is also impossible to use this method to separate different adjacent bone structures from one another, for example in order to separate the joint cavity in the pelvis structure from the joint head of the femur when scanning a hip joint, and to view these object elements separately. Furthermore, a simple threshold value method such as this often cannot be used reliably due to so-called partial volume effects and metal artifacts, which ensure that parts of the surface of the object element to be separated cannot be determined.
Thus, in many cases, only manual segmentation of the section image data is possible. Unfortunately, however, such manual segmentation is often very difficult to carry out owing to the complicated anatomy of the examination object, and is associated with a high time penalty.
In principle, the segmentation process can be improved by using so-called model-based methods, in which morphological knowledge of the examination object is included in the segmentation process.
Virtual models are already used in many fields of technology in order to simulate objects in specific states or to recognize objects again. By way of example, US 2001/0026272 A1 proposes a simulation method in which, taking into account mechanical and visual material characteristics of a piece of clothing, the physically correct fit of the relevant piece of clothing on the human body can be simulated. Furthermore, for example, U.S. Pat. No. 6,002,782 discloses a method for personal identification based on the correlation of recorded 2D images of the human face with simulated 2D images of already known optical 3D face scans, with the x axis of the 3D face scan being made to match the viewing direction of the camera associated with the 2D image.
The medical field, as well, already makes use of methods in order to produce virtual models of objects to be examined, on the basis of widely differing measurements, and these models can then be used as the basis for the further examination of the relevant object. By way of example, U.S. Pat. No. 6,028,907 describes a method in which a three-dimensional model of a spinal column to be examined is produced from two-dimensional CT section images and two-dimensional CT scout images.
In the case of the present problems of segmentation of section image data, image data which is missing, for example as a result of partial volume effects or metal artifacts, can be compensated for by matching a model to a target structure in the section image data (which includes the object element to be separated) in individual layers. This allows the complete reconstruction of the object element to be separated, for example the organ or the specific bone structure. However, in this method, the problem of segmentation is in the end changed to the problem of matching a model as well as possible to a target structure in the section image data.